학술논문
Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.
Document Type
Article
Source
Subject
*BIG data
*EPIDEMICS
*DEEP learning
*BOX-Jenkins forecasting
*DECISION making
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Language
ISSN
2077-0383
Abstract
Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences. [ABSTRACT FROM AUTHOR]